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AI-Casanova
2023-12-16 20:16:01 -06:00
parent 2020d20bcb
commit 9e7757b7fe
3 changed files with 19 additions and 19 deletions
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modules/processing_diffusers.py
+10 -13
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@@ -89,8 +89,13 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if kwargs.get('latents', None) is None:
return kwargs
kwargs = correction_callback(p, timestep, kwargs)
kwargs["prompt_embeds"] = p.prompt_embeds[step-1]
kwargs["negative_prompt_embeds"] = p.negative_embeds[step-1]
try:
kwargs["prompt_embeds"] = p.prompt_embeds[step + 1].repeat(1, kwargs["prompt_embeds"].shape[0], 1).view(
kwargs["prompt_embeds"].shape[0], kwargs["prompt_embeds"].shape[1], -1)
kwargs["negative_prompt_embeds"] = p.negative_embeds[step + 1].repeat(1, kwargs["negative_prompt_embeds"].shape[0], 1).view(
kwargs["negative_prompt_embeds"].shape[0], kwargs["negative_prompt_embeds"].shape[1], -1)
except:
pass
shared.state.current_latent = kwargs['latents']
if shared.cmd_opts.profile and shared.profiler is not None:
shared.profiler.step()
@@ -295,10 +300,6 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
possible = signature.parameters.keys()
generator_device = devices.cpu if shared.opts.diffusers_generator_device == "cpu" else shared.device
generator = [torch.Generator(generator_device).manual_seed(s) for s in seeds]
# prompt_embed = None
# pooled = None
# negative_embed = None
# negative_pooled = None
prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2)
parser = 'Fixed attention'
if shared.opts.prompt_attention != 'Fixed attention' and 'StableDiffusion' in model.__class__.__name__:
@@ -312,20 +313,16 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
errors.display(e, 'Prompt parser encode')
if 'prompt' in possible:
if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and p.prompt_embeds[0] is not None:
# if type(pooled) == list:
# pooled = pooled[0]
# if type(negative_pooled) == list:
# negative_pooled = p.negative_pooleds[0][0]
args['prompt_embeds'] = p.prompt_embeds[0]
if 'XL' in model.__class__.__name__:
args['pooled_prompt_embeds'] = p.positive_pooleds[0][0]
args['pooled_prompt_embeds'] = p.positive_pooleds[0]
else:
args['prompt'] = prompts
if 'negative_prompt' in possible:
if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and p.negative_embeds[0] is not None:
args['negative_prompt_embeds'] = p.negative_embeds[0]
if 'XL' in model.__class__.__name__:
args['negative_pooled_prompt_embeds'] = p.negative_pooleds[0][0]
args['negative_pooled_prompt_embeds'] = p.negative_pooleds[0]
else:
args['negative_prompt'] = negative_prompts
if hasattr(model, 'scheduler') and hasattr(model.scheduler, 'noise_sampler_seed') and hasattr(model.scheduler, 'noise_sampler'):
@@ -345,7 +342,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
args['callback'] = diffusers_callback_legacy
elif 'callback_on_step_end_tensor_inputs' in possible:
args['callback_on_step_end'] = diffusers_callback
args['callback_on_step_end_tensor_inputs'] = ['latents']
args['callback_on_step_end_tensor_inputs'] = ['latents', 'prompt_embeds', 'negative_prompt_embeds']
for arg in kwargs:
if arg in possible: # add kwargs
args[arg] = kwargs[arg]